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arXiv 2409.14873math.OCcs.SYeess.SY
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最优状态估计:Turnpike 分析与性能结果

Optimal state estimation: Turnpike analysis and performance results

University of Bayreuth(拜罗伊特大学)

Julian D. Schiller, Lars Grüne, Matthias A. Müller

AI总结:

本文研究最优状态估计中的 turnpike 现象,证明全数据问题最优解可引导截断问题,并给出由衰减敏感性保证的近似最优性能。

摘要:

本文在最优状态估计背景下引入 turnpike 论证。具体而言,我们证明:涉及所有可用历史数据的状态估计问题的最优解,可作为仅涉及数据子集的截断问题解的 turnpike。我们对这一现象进行数学形式化,并基于底层非线性规划的衰减敏感性性质导出一个充分条件。作为第二项贡献,我们展示如何利用特定 turnpike 性质,在通过一系列截断问题逼近完整问题最优解时建立性能保证;并证明所得性能(包括平均与非平均情形)近似最优,其误差项可通过适当选择时域长度而任意小。此外,我们讨论这些结果对实际相关的滚动时域估计情形的有意义启示,并通过一个数值算例说明所得结果。

英文摘要:

In this paper, we introduce turnpike arguments in the context of optimal state estimation. In particular, we show that the optimal solution of the state estimation problem involving all available past data serves as turnpike for the solutions of truncated problems involving only a subset of the data. We mathematically formalize this phenomenon and derive a sufficient condition that relies on a decaying sensitivity property of the underlying nonlinear program. As second contribution, we show how a specific turnpike property can be used to establish performance guarantees when approximating the optimal solution of the full problem by a sequence of truncated problems, and we show that the resulting performance (both averaged and non-averaged) is approximately optimal with error terms that can be made arbitrarily small by an appropriate choice of the horizon length. In addition, we discuss interesting implications of these results for the practically relevant case of moving horizon estimation and illustrate our results with a numerical example.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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